import pandas as pd
import plotly.express as px
#html export
import plotly.io as pio
pio.renderers.default = 'notebook'
df = pd.read_csv('2019-census-report.csv', decimal=',')
df
| County | Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baringo | 666763 | 336322 | 330428 | 142518 | 5 | 10976 | 61 | 555561 | 111202 | 13 |
| 1 | Bomet | 875689 | 434287 | 441379 | 187641 | 5 | 2531 | 346 | 724186 | 151503 | 23 |
| 2 | Bungoma | 1670570 | 812146 | 858389 | 358796 | 5 | 3024 | 552 | 1630934 | 39636 | 35 |
| 3 | Busia | 893681 | 426252 | 467401 | 198152 | 5 | 1696 | 527 | 488075 | 405606 | 28 |
| 4 | Elgeyo-Marakwet | 454480 | 227317 | 227151 | 99861 | 5 | 3032 | 150 | 369998 | 84482 | 12 |
| 5 | Embu | 608599 | 304208 | 304367 | 182743 | 3 | 2821 | 216 | 516212 | 92387 | 24 |
| 6 | Garissa | 841353 | 458975 | 382344 | 141394 | 6 | 44736 | 19 | 623060 | 218293 | 34 |
| 7 | Homa Bay | 1131950 | 539560 | 592367 | 262036 | 4 | 3153 | 359 | 963794 | 168156 | 23 |
| 8 | Isiolo | 268002 | 139510 | 128483 | 58072 | 5 | 25350 | 11 | 143294 | 124708 | 9 |
| 9 | Kajiado | 1117840 | 557098 | 560704 | 316179 | 4 | 21871 | 51 | 687312 | 430528 | 38 |
| 10 | Kakamega | 1867579 | 897133 | 970406 | 433207 | 4 | 3020 | 618 | 1660651 | 206928 | 40 |
| 11 | Kericho | 901777 | 450751 | 451008 | 206036 | 4 | 2436 | 370 | 758339 | 143438 | 28 |
| 12 | Kiambu | 2417735 | 1187146 | 1230454 | 795241 | 3 | 2539 | 952 | 1623282 | 794453 | 135 |
| 13 | Kilifi | 1453787 | 704089 | 749673 | 298472 | 5 | 12540 | 116 | 1109735 | 344052 | 25 |
| 14 | Kirinyaga | 610411 | 302011 | 308369 | 204188 | 3 | 1478 | 413 | 528054 | 82357 | 31 |
| 15 | Kisii | 1266860 | 605784 | 661038 | 308054 | 4 | 1323 | 958 | 1152282 | 114578 | 38 |
| 16 | Kisumu | 1155574 | 560942 | 594609 | 300745 | 4 | 2085 | 554 | 968909 | 186665 | 23 |
| 17 | Kitui | 1136187 | 549003 | 587151 | 262942 | 4 | 30430 | 37 | 1012709 | 123478 | 33 |
| 18 | Kwale | 866820 | 425121 | 441681 | 173176 | 5 | 8267 | 105 | 649931 | 216889 | 18 |
| 19 | Laikipia | 518580 | 259440 | 259102 | 149271 | 3 | 9532 | 54 | 399227 | 119353 | 18 |
| 20 | Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 |
| 21 | Machakos | 1421932 | 710707 | 711191 | 402466 | 4 | 6043 | 235 | 1098584 | 323348 | 34 |
| 22 | Makueni | 987653 | 489691 | 497942 | 244669 | 4 | 8170 | 121 | 884527 | 103126 | 20 |
| 23 | Mandera | 867457 | 434976 | 432444 | 125763 | 7 | 25940 | 33 | 1025756 | -158299 | 37 |
| 24 | Marsabit | 459785 | 243548 | 216219 | 77495 | 6 | 70944 | 6 | 291166 | 168619 | 18 |
| 25 | Meru | 1545714 | 767698 | 777975 | 426360 | 4 | 7006 | 221 | 1356301 | 189413 | 41 |
| 26 | Migori | 1116436 | 536187 | 580214 | 240168 | 5 | 2614 | 427 | 917170 | 199266 | 35 |
| 27 | Mombasa | 1208333 | 610257 | 598046 | 378422 | 3 | 220 | 5495 | 939370 | 268963 | 30 |
| 28 | Murang'a | 1056640 | 523940 | 532669 | 318105 | 3 | 2524 | 419 | 942581 | 114059 | 31 |
| 29 | Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
| 30 | Nakuru | 2162202 | 1077272 | 1084835 | 616046 | 4 | 7462 | 290 | 1603325 | 558877 | 95 |
| 31 | Nandi | 885711 | 441259 | 444430 | 199426 | 4 | 2856 | 310 | 752965 | 132746 | 22 |
| 32 | Narok | 1157873 | 579042 | 578805 | 241125 | 5 | 17950 | 65 | 850920 | 306953 | 26 |
| 33 | Nyamira | 605576 | 290907 | 314656 | 150669 | 4 | 897 | 675 | 598252 | 7324 | 13 |
| 34 | Nyandarua | 638289 | 315022 | 323247 | 179686 | 4 | 3286 | 194 | 596268 | 42021 | 20 |
| 35 | Nyeri | 759164 | 374288 | 384845 | 248050 | 3 | 3325 | 228 | 693558 | 65606 | 31 |
| 36 | Samburu | 310327 | 156774 | 153546 | 65910 | 5 | 21065 | 15 | 223947 | 86380 | 7 |
| 37 | Siaya | 993183 | 471669 | 521496 | 250698 | 4 | 2530 | 393 | 842304 | 150879 | 18 |
| 38 | Taita Taveta | 340671 | 173337 | 167327 | 96429 | 4 | 17152 | 20 | 284657 | 56014 | 7 |
| 39 | Tana River | 315943 | 158550 | 157391 | 68242 | 5 | 37951 | 8 | 240075 | 75868 | 2 |
| 40 | Tharaka-Nithi | 393177 | 193764 | 199406 | 109860 | 4 | 2564 | 153 | 365330 | 27847 | 7 |
| 41 | Trans Nzoia | 990341 | 489107 | 501206 | 223808 | 4 | 2495 | 397 | 818757 | 171584 | 28 |
| 42 | Turkana | 926976 | 478087 | 448868 | 164519 | 6 | 68233 | 14 | 855399 | 71577 | 21 |
| 43 | Uasin Gishu | 1163186 | 580269 | 582889 | 304943 | 4 | 3392 | 343 | 894179 | 269007 | 28 |
| 44 | Vihiga | 590013 | 283678 | 306323 | 143365 | 4 | 564 | 1047 | 554622 | 35391 | 12 |
| 45 | Wajir | 781263 | 415374 | 365840 | 127932 | 6 | 56773 | 14 | 661941 | 119322 | 49 |
| 46 | West Pokot | 621241 | 307013 | 314213 | 116182 | 5 | 9123 | 68 | 512690 | 108551 | 15 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 47 entries, 0 to 46 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 County 47 non-null object 1 Total_Population19 47 non-null int64 2 Male population 2019 47 non-null int64 3 Female population 2019 47 non-null int64 4 Households 47 non-null int64 5 Av_HH_Size 47 non-null int64 6 LandArea 47 non-null int64 7 Population Density 47 non-null int64 8 Population in 2009 47 non-null int64 9 Pop_change 47 non-null int64 10 Intersex population 2019 47 non-null int64 dtypes: int64(10), object(1) memory usage: 4.2+ KB
# Setting County as the index for the table
df.index = df['County']
df = df.drop(columns='County')
df
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Baringo | 666763 | 336322 | 330428 | 142518 | 5 | 10976 | 61 | 555561 | 111202 | 13 |
| Bomet | 875689 | 434287 | 441379 | 187641 | 5 | 2531 | 346 | 724186 | 151503 | 23 |
| Bungoma | 1670570 | 812146 | 858389 | 358796 | 5 | 3024 | 552 | 1630934 | 39636 | 35 |
| Busia | 893681 | 426252 | 467401 | 198152 | 5 | 1696 | 527 | 488075 | 405606 | 28 |
| Elgeyo-Marakwet | 454480 | 227317 | 227151 | 99861 | 5 | 3032 | 150 | 369998 | 84482 | 12 |
| Embu | 608599 | 304208 | 304367 | 182743 | 3 | 2821 | 216 | 516212 | 92387 | 24 |
| Garissa | 841353 | 458975 | 382344 | 141394 | 6 | 44736 | 19 | 623060 | 218293 | 34 |
| Homa Bay | 1131950 | 539560 | 592367 | 262036 | 4 | 3153 | 359 | 963794 | 168156 | 23 |
| Isiolo | 268002 | 139510 | 128483 | 58072 | 5 | 25350 | 11 | 143294 | 124708 | 9 |
| Kajiado | 1117840 | 557098 | 560704 | 316179 | 4 | 21871 | 51 | 687312 | 430528 | 38 |
| Kakamega | 1867579 | 897133 | 970406 | 433207 | 4 | 3020 | 618 | 1660651 | 206928 | 40 |
| Kericho | 901777 | 450751 | 451008 | 206036 | 4 | 2436 | 370 | 758339 | 143438 | 28 |
| Kiambu | 2417735 | 1187146 | 1230454 | 795241 | 3 | 2539 | 952 | 1623282 | 794453 | 135 |
| Kilifi | 1453787 | 704089 | 749673 | 298472 | 5 | 12540 | 116 | 1109735 | 344052 | 25 |
| Kirinyaga | 610411 | 302011 | 308369 | 204188 | 3 | 1478 | 413 | 528054 | 82357 | 31 |
| Kisii | 1266860 | 605784 | 661038 | 308054 | 4 | 1323 | 958 | 1152282 | 114578 | 38 |
| Kisumu | 1155574 | 560942 | 594609 | 300745 | 4 | 2085 | 554 | 968909 | 186665 | 23 |
| Kitui | 1136187 | 549003 | 587151 | 262942 | 4 | 30430 | 37 | 1012709 | 123478 | 33 |
| Kwale | 866820 | 425121 | 441681 | 173176 | 5 | 8267 | 105 | 649931 | 216889 | 18 |
| Laikipia | 518580 | 259440 | 259102 | 149271 | 3 | 9532 | 54 | 399227 | 119353 | 18 |
| Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 |
| Machakos | 1421932 | 710707 | 711191 | 402466 | 4 | 6043 | 235 | 1098584 | 323348 | 34 |
| Makueni | 987653 | 489691 | 497942 | 244669 | 4 | 8170 | 121 | 884527 | 103126 | 20 |
| Mandera | 867457 | 434976 | 432444 | 125763 | 7 | 25940 | 33 | 1025756 | -158299 | 37 |
| Marsabit | 459785 | 243548 | 216219 | 77495 | 6 | 70944 | 6 | 291166 | 168619 | 18 |
| Meru | 1545714 | 767698 | 777975 | 426360 | 4 | 7006 | 221 | 1356301 | 189413 | 41 |
| Migori | 1116436 | 536187 | 580214 | 240168 | 5 | 2614 | 427 | 917170 | 199266 | 35 |
| Mombasa | 1208333 | 610257 | 598046 | 378422 | 3 | 220 | 5495 | 939370 | 268963 | 30 |
| Murang'a | 1056640 | 523940 | 532669 | 318105 | 3 | 2524 | 419 | 942581 | 114059 | 31 |
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
| Nakuru | 2162202 | 1077272 | 1084835 | 616046 | 4 | 7462 | 290 | 1603325 | 558877 | 95 |
| Nandi | 885711 | 441259 | 444430 | 199426 | 4 | 2856 | 310 | 752965 | 132746 | 22 |
| Narok | 1157873 | 579042 | 578805 | 241125 | 5 | 17950 | 65 | 850920 | 306953 | 26 |
| Nyamira | 605576 | 290907 | 314656 | 150669 | 4 | 897 | 675 | 598252 | 7324 | 13 |
| Nyandarua | 638289 | 315022 | 323247 | 179686 | 4 | 3286 | 194 | 596268 | 42021 | 20 |
| Nyeri | 759164 | 374288 | 384845 | 248050 | 3 | 3325 | 228 | 693558 | 65606 | 31 |
| Samburu | 310327 | 156774 | 153546 | 65910 | 5 | 21065 | 15 | 223947 | 86380 | 7 |
| Siaya | 993183 | 471669 | 521496 | 250698 | 4 | 2530 | 393 | 842304 | 150879 | 18 |
| Taita Taveta | 340671 | 173337 | 167327 | 96429 | 4 | 17152 | 20 | 284657 | 56014 | 7 |
| Tana River | 315943 | 158550 | 157391 | 68242 | 5 | 37951 | 8 | 240075 | 75868 | 2 |
| Tharaka-Nithi | 393177 | 193764 | 199406 | 109860 | 4 | 2564 | 153 | 365330 | 27847 | 7 |
| Trans Nzoia | 990341 | 489107 | 501206 | 223808 | 4 | 2495 | 397 | 818757 | 171584 | 28 |
| Turkana | 926976 | 478087 | 448868 | 164519 | 6 | 68233 | 14 | 855399 | 71577 | 21 |
| Uasin Gishu | 1163186 | 580269 | 582889 | 304943 | 4 | 3392 | 343 | 894179 | 269007 | 28 |
| Vihiga | 590013 | 283678 | 306323 | 143365 | 4 | 564 | 1047 | 554622 | 35391 | 12 |
| Wajir | 781263 | 415374 | 365840 | 127932 | 6 | 56773 | 14 | 661941 | 119322 | 49 |
| West Pokot | 621241 | 307013 | 314213 | 116182 | 5 | 9123 | 68 | 512690 | 108551 | 15 |
Total Male population and Total Female population comparison
# creating a series for required values
mvf = pd.Series([df['Male population 2019'].sum(),df['Female population 2019'].sum()], index=['Total Male population','Total Female population'])
mvf
Total Male population 23548066 Total Female population 24014716 dtype: int64
# using the series to plot a pie chart
fig = px.pie(names=mvf.index, values=mvf)
fig.update_layout(title='Male vs Female Population')
fig.show()
Determining Population growth
# creating a series with required values for graph
pop_change = pd.Series([df['Population in 2009'].sum(),df['Total_Population19'].sum()], index=['Population in 2009', 'Population in 2019'])
pop_change
Population in 2009 38610097 Population in 2019 47564316 dtype: int64
# Creating a bar graph using Plotly Express
fig = px.bar(x=pop_change.index, y=pop_change, title='Population Change (2009-2019)')
fig.update_layout(yaxis_title='Population')
# Show the plot
fig.show()
# population growth %
((df['Total_Population19'].sum() - df['Population in 2009'].sum())/ df['Population in 2009'].sum())*100
23.191392137657672
Population Growth is at 23% over 10 years
# County with the largest population
df[df['Total_Population19'] == df['Total_Population19'].max()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
# County with the largest male population
df[df['Male population 2019'] == df['Male population 2019'].max()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
# County with the largest female population
df[df['Female population 2019'] == df['Female population 2019'].max()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
Nairobi has both the largest male and female population
# County with the smallest population
df[df['Total_Population19'] == df['Total_Population19'].min()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 |
# County with the smallest male population
df[df['Male population 2019'] == df['Male population 2019'].min()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 |
# County with the largest female population
df[df['Female population 2019'] == df['Female population 2019'].min()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 |
Lamu has both the smallest male and female population
df['Av_HH_Size'].mean()
4.340425531914893
Average number of people in each household is 4 people
# County with highest population change
df[df['Pop_change'] == df['Pop_change'].max()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 |
# creating a series with required values for graph
nai_pop_change = df.loc['Nairobi', ['Total_Population19', 'Population in 2009', 'Pop_change']]
nai_pop_change = pd.Series(nai_pop_change)
print(nai_pop_change)
print()
print((nai_pop_change[2]/nai_pop_change[0])*100)
'''
nai_pop_change = pd.Series([df['Total_Population19'].max(),df['Population in 2009'].max()], index=['Total Population', 'Population in 2019'])
print(nai_pop_change)
((nai_pop_change[0]-nai_pop_change[1])/nai_pop_change[0])*100
'''
Total_Population19 4397073 Population in 2009 3138369 Pop_change 1258704 Name: Nairobi, dtype: int64 28.625951854790678
"\nnai_pop_change = pd.Series([df['Total_Population19'].max(),df['Population in 2009'].max()], index=['Total Population', 'Population in 2019'])\nprint(nai_pop_change)\n((nai_pop_change[0]-nai_pop_change[1])/nai_pop_change[0])*100\n"
Nairobi has the largest population growth at +1,258,704 with the percentage growth at 29%
# County with least population change
df[df['Pop_change'] == df['Pop_change'].min()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| Mandera | 867457 | 434976 | 432444 | 125763 | 7 | 25940 | 33 | 1025756 | -158299 | 37 |
man_pop_change = df.loc['Mandera', ['Total_Population19', 'Population in 2009', 'Pop_change']]
man_pop_change = pd.Series(man_pop_change)
print(man_pop_change)
(man_pop_change[2]/man_pop_change[0])*100
Total_Population19 867457 Population in 2009 1025756 Pop_change -158299 Name: Mandera, dtype: int64
-18.248627885877916
Mandera has the smallest population growth at -158,299 with the percentage growth at -18%
df['%_Growth'] = (df['Pop_change']/df['Total_Population19'])*100
df
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | %_Growth | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| County | |||||||||||
| Baringo | 666763 | 336322 | 330428 | 142518 | 5 | 10976 | 61 | 555561 | 111202 | 13 | 16.677890 |
| Bomet | 875689 | 434287 | 441379 | 187641 | 5 | 2531 | 346 | 724186 | 151503 | 23 | 17.301005 |
| Bungoma | 1670570 | 812146 | 858389 | 358796 | 5 | 3024 | 552 | 1630934 | 39636 | 35 | 2.372603 |
| Busia | 893681 | 426252 | 467401 | 198152 | 5 | 1696 | 527 | 488075 | 405606 | 28 | 45.385993 |
| Elgeyo-Marakwet | 454480 | 227317 | 227151 | 99861 | 5 | 3032 | 150 | 369998 | 84482 | 12 | 18.588717 |
| Embu | 608599 | 304208 | 304367 | 182743 | 3 | 2821 | 216 | 516212 | 92387 | 24 | 15.180275 |
| Garissa | 841353 | 458975 | 382344 | 141394 | 6 | 44736 | 19 | 623060 | 218293 | 34 | 25.945471 |
| Homa Bay | 1131950 | 539560 | 592367 | 262036 | 4 | 3153 | 359 | 963794 | 168156 | 23 | 14.855426 |
| Isiolo | 268002 | 139510 | 128483 | 58072 | 5 | 25350 | 11 | 143294 | 124708 | 9 | 46.532489 |
| Kajiado | 1117840 | 557098 | 560704 | 316179 | 4 | 21871 | 51 | 687312 | 430528 | 38 | 38.514278 |
| Kakamega | 1867579 | 897133 | 970406 | 433207 | 4 | 3020 | 618 | 1660651 | 206928 | 40 | 11.080013 |
| Kericho | 901777 | 450751 | 451008 | 206036 | 4 | 2436 | 370 | 758339 | 143438 | 28 | 15.906150 |
| Kiambu | 2417735 | 1187146 | 1230454 | 795241 | 3 | 2539 | 952 | 1623282 | 794453 | 135 | 32.859391 |
| Kilifi | 1453787 | 704089 | 749673 | 298472 | 5 | 12540 | 116 | 1109735 | 344052 | 25 | 23.665915 |
| Kirinyaga | 610411 | 302011 | 308369 | 204188 | 3 | 1478 | 413 | 528054 | 82357 | 31 | 13.492057 |
| Kisii | 1266860 | 605784 | 661038 | 308054 | 4 | 1323 | 958 | 1152282 | 114578 | 38 | 9.044251 |
| Kisumu | 1155574 | 560942 | 594609 | 300745 | 4 | 2085 | 554 | 968909 | 186665 | 23 | 16.153444 |
| Kitui | 1136187 | 549003 | 587151 | 262942 | 4 | 30430 | 37 | 1012709 | 123478 | 33 | 10.867753 |
| Kwale | 866820 | 425121 | 441681 | 173176 | 5 | 8267 | 105 | 649931 | 216889 | 18 | 25.021227 |
| Laikipia | 518580 | 259440 | 259102 | 149271 | 3 | 9532 | 54 | 399227 | 119353 | 18 | 23.015350 |
| Lamu | 143920 | 76103 | 67813 | 37963 | 4 | 6253 | 23 | 101539 | 42381 | 4 | 29.447610 |
| Machakos | 1421932 | 710707 | 711191 | 402466 | 4 | 6043 | 235 | 1098584 | 323348 | 34 | 22.740047 |
| Makueni | 987653 | 489691 | 497942 | 244669 | 4 | 8170 | 121 | 884527 | 103126 | 20 | 10.441521 |
| Mandera | 867457 | 434976 | 432444 | 125763 | 7 | 25940 | 33 | 1025756 | -158299 | 37 | -18.248628 |
| Marsabit | 459785 | 243548 | 216219 | 77495 | 6 | 70944 | 6 | 291166 | 168619 | 18 | 36.673445 |
| Meru | 1545714 | 767698 | 777975 | 426360 | 4 | 7006 | 221 | 1356301 | 189413 | 41 | 12.254078 |
| Migori | 1116436 | 536187 | 580214 | 240168 | 5 | 2614 | 427 | 917170 | 199266 | 35 | 17.848403 |
| Mombasa | 1208333 | 610257 | 598046 | 378422 | 3 | 220 | 5495 | 939370 | 268963 | 30 | 22.259013 |
| Murang'a | 1056640 | 523940 | 532669 | 318105 | 3 | 2524 | 419 | 942581 | 114059 | 31 | 10.794500 |
| Nairobi | 4397073 | 2192452 | 2204376 | 1506888 | 3 | 704 | 6247 | 3138369 | 1258704 | 245 | 28.625952 |
| Nakuru | 2162202 | 1077272 | 1084835 | 616046 | 4 | 7462 | 290 | 1603325 | 558877 | 95 | 25.847585 |
| Nandi | 885711 | 441259 | 444430 | 199426 | 4 | 2856 | 310 | 752965 | 132746 | 22 | 14.987507 |
| Narok | 1157873 | 579042 | 578805 | 241125 | 5 | 17950 | 65 | 850920 | 306953 | 26 | 26.510075 |
| Nyamira | 605576 | 290907 | 314656 | 150669 | 4 | 897 | 675 | 598252 | 7324 | 13 | 1.209427 |
| Nyandarua | 638289 | 315022 | 323247 | 179686 | 4 | 3286 | 194 | 596268 | 42021 | 20 | 6.583382 |
| Nyeri | 759164 | 374288 | 384845 | 248050 | 3 | 3325 | 228 | 693558 | 65606 | 31 | 8.641874 |
| Samburu | 310327 | 156774 | 153546 | 65910 | 5 | 21065 | 15 | 223947 | 86380 | 7 | 27.835155 |
| Siaya | 993183 | 471669 | 521496 | 250698 | 4 | 2530 | 393 | 842304 | 150879 | 18 | 15.191460 |
| Taita Taveta | 340671 | 173337 | 167327 | 96429 | 4 | 17152 | 20 | 284657 | 56014 | 7 | 16.442257 |
| Tana River | 315943 | 158550 | 157391 | 68242 | 5 | 37951 | 8 | 240075 | 75868 | 2 | 24.013192 |
| Tharaka-Nithi | 393177 | 193764 | 199406 | 109860 | 4 | 2564 | 153 | 365330 | 27847 | 7 | 7.082561 |
| Trans Nzoia | 990341 | 489107 | 501206 | 223808 | 4 | 2495 | 397 | 818757 | 171584 | 28 | 17.325749 |
| Turkana | 926976 | 478087 | 448868 | 164519 | 6 | 68233 | 14 | 855399 | 71577 | 21 | 7.721559 |
| Uasin Gishu | 1163186 | 580269 | 582889 | 304943 | 4 | 3392 | 343 | 894179 | 269007 | 28 | 23.126740 |
| Vihiga | 590013 | 283678 | 306323 | 143365 | 4 | 564 | 1047 | 554622 | 35391 | 12 | 5.998342 |
| Wajir | 781263 | 415374 | 365840 | 127932 | 6 | 56773 | 14 | 661941 | 119322 | 49 | 15.272962 |
| West Pokot | 621241 | 307013 | 314213 | 116182 | 5 | 9123 | 68 | 512690 | 108551 | 15 | 17.473251 |
# County with highest percentage population change
df[df['%_Growth'] == df['%_Growth'].max()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | %_Growth | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| County | |||||||||||
| Isiolo | 268002 | 139510 | 128483 | 58072 | 5 | 25350 | 11 | 143294 | 124708 | 9 | 46.532489 |
Isiolo has the largest percentage population growth at 47%
# County with lowest percentage population change
df[df['%_Growth'] == df['%_Growth'].min()]
| Total_Population19 | Male population 2019 | Female population 2019 | Households | Av_HH_Size | LandArea | Population Density | Population in 2009 | Pop_change | Intersex population 2019 | %_Growth | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| County | |||||||||||
| Mandera | 867457 | 434976 | 432444 | 125763 | 7 | 25940 | 33 | 1025756 | -158299 | 37 | -18.248628 |
Mandera has the lowest percentage population growth at -18%